Lactate dehydrogenases promote glioblastoma growth and invasion via a metabolic symbiosis
Bibliographic record
Abstract
Abstract Lactate is a central metabolite in brain physiology, involved in the astrocyte-neuron lactate shuttle, but also contributes to tumor development. Glioblastoma (GBM) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. By adapting their glycolytic or oxidative metabolism, GBM stem-like cells are able to resist chemo- and radiotherapy. We show herein that lactate fuels GBM anaplerosis by replenishing the TCA cycle in absence of glucose. Lactate dehydrogenases (LDH) catalyze the interconversion of pyruvate and lactate. Deletion of either LDHA or LDHB did not alter significantly GBM growth and invasion. However, ablation of both LDH isoforms led to a reduction of tumor growth, and, consequently, to an increase in mouse survival. Comparative transcriptomics and metabolomics revealed metabolic rewiring involving high oxidative phosphorylation (OxPhos) in the double LDHA/B KO group which sensitized tumors to cranial irradiation, massively improving mouse survival. Survival was also increased when control mice were treated with the antiangiogenic treatment, bevacizumab, and the antiepileptic drug, stiripentol which targets LDH activity. Taken together, this highlights the complex metabolic network in which both LDH A and B are integrated and underscores that combined inhibition of LDHA and B is necessary to impact tumor development. Targeting of these enzymes in combination with anti-angiogenic and repurposed drugs may be of therapeutic benefit, especially when associated with radiotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".